• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.3
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• pp.262-276
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• 2018

Models to predict Leaf Wetness Duration (LWD) were evaluated using the observed meteorological and dew data at the 11 citrus orchards in Jeju, South Korea from 2016 to 2017. The sensitivity and the prediction accuracy were evaluated with four models (i.e., Number of Hours of Relative Humidity (NHRH), Classification And Regression Tree/Stepwise Linear Discriminant (CART/SLD), Penman-Monteith (PM), Deep-learning Neural Network (DNN)). The sensitivity of models was evaluated with rainfall and seasonal changes. When the data in rainy days were excluded from the whole data set, the LWD models had smaller average error (Root Mean Square Error (RMSE) about 1.5hours). The seasonal error of the DNN model had the similar magnitude (RMSE about 3 hours) among all seasons excluding winter. The other models had the greatest error in summer (RMSE about 9.6 hours) and the lowest error in winter (RMSE about 3.3 hours). These models were also evaluated by the statistical error analysis method and the regression analysis method of mean squared deviation. The DNN model had the best performance by statistical error whereas the CART/SLD model had the worst prediction accuracy. The Mean Square Deviation (MSD) is a method of analyzing the linearity of a model with three components: squared bias (SB), nonunity slope (NU), and lack of correlation (LC). Better model performance was determined by lower SB and LC and higher NU. The results of MSD analysis indicated that the DNN model would provide the best performance and followed by the PM, the NHRH and the CART/SLD in order. This result suggested that the machine learning model would be useful to improve the accuracy of agricultural information using meteorological data.

• Water for future
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• v.28 no.5
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• pp.219-233
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• 1995

A non-stationary multivariate model is selected in which the mean and variance of rainfall are not temporally or spatially constant. And the rainfall prediction system is constructed which uses the recursive estimation algorithm, Kalman filter, to estimate system states and parameters of rainfall model simulataneously. The on-line, real-time, multivariate short-term, rainfall prediction for multi-stations and lead-times is carried out through the estimation of non-stationary mean and variance by the storm counter method, the normalized residual covariance and rainfall speed. The results of rainfall prediction system model agree with those generated by non-stationary multivariate model. The longer the lead time is, the larger the root mean square error becomes and the further the model efficiency decreases form 1. Thus, the accuracy of the rainfall prediction decreases as the lead time gets longer. Also it shows that the mean obtained by storm counter method constitutes the most significant part of the rainfall structure.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.419-430
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• 2014

An accurate prediction of electricity supply and demand is important for daily life, industrial activities, and national management. In this paper electricity sales is predicted by time series modelling. Real data analysis shows the transfer function model with cooling and heating days as an input time series and a pulse function as an intervention variable outperforms other time series models for the root mean square error and the mean absolute percentage error.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.513-522
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• 2014

Using the assumption that the price of a stock follows a geometric Brownian motion with constant volatility, Black and Scholes (BS) derived a formula that gives the price of a European call option on the stock as a function of the stock price, the strike price, the time to maturity, the risk-free interest rate, the dividend rate paid by the stock, and the volatility of the stock's return. However, implied volatilities of BS method tend to depend on the stock prices and the time to maturity in practice. To address this shortcoming, we estimate the implied volatility function as a function of the strike priceand the time to maturity for data consisting of the daily prices for KOSPI200 call options from January 2007 to May 2009 using support vector regression (SVR), the multiple additive regression trees (MART) algorithm, and ordinary least squaress (OLS) regression. In conclusion, use of MART or SVR in the BS pricing model reduced both RMSE and MAE, compared to the OLS-based BS pricing model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1B
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• pp.1-7
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• 2012

Quasi-two dimensional numerical model (GSTARS) was applied to determine the optimal sediment discharge formula for simulating the sedimentologic characteristics of Hyeongsan river. The field measurements have been conducted to obtain the data, such as sediment discharge, bed material, and channel geometry, for model calibration and verification. The sediment discharge formulas, which have been generally used, have been assessed according to the average error, relative error, RMSE, RRMSE, discrepancy ratio and Nash-Sutcliffe efficiency coefficient for bed changes along the thalweg. From the results, Laursen formula(1958) shows the best performance to simulate the long-term bed change of Hyeongsan river.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.509-520
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• 2023

Various drought indices are widely used for assessing drought conditions which are affected by many factors such as precipitation, soil moisture, and runoff. The values of drought indices varies depending on hydro-meteorological data and calculation formulas, and the judgment of the drought condition may also vary. This study selected four calculation components such as precipitation data length, accumulation period, probability distribution function, and parameter estimation method as the sources of uncertainty in the calculation of standardized precipitation index (SPI), and evaluated their contributions to the uncertainty using root mean square error (RMSE) and linear mixed model (LMM). The RMSE estimated the overall errors in the SPI calculation, and the LMM was used to quantify the uncertainty contribution of each factor. The results showed that as the accumulation period increased and the data period extended, the RMSEs decreased. The comparison of relative uncertainty using LMM indicated that the sample size had the greatest impact on the SPI calculation. In addition, as sample size increased, the relative uncertainty related to the sample size used for SPI calculation decreased and the relative uncertainty associated with accumulation period and parameter estimation increased. In conclusion, to reduce the uncertainty in the SPI calculation, it is essential to collect long-term data first, followed by the appropriate selection of probability distribution models and parameter estimation methods that represent well the data characteristics.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.3
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• pp.271-279
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• 2006

Acoustic Emission (AE) technology is an effective nondestructive testing for continuous monitoring of defect formation and failures in structural materials. This paper presents a source location model using Acoustic Emission (AE) sensors in a Pre-Stressed Concrete (PSC) beam and the evaluation of the model was performed through lab experiments. 54 AE events were made on the surface of the 5m-PSC beam using a Schmidt Hammer and arrival times were measured with 7AE sensors. The source location f3r each event was estimated using least squares. The results were compared with actual positions and the RMSE (Root Mean Square Errors) was about 2cm.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1503-1506
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• 2007

수공구조물의 설계를 위해서 주로 사용되는 강우강도식은 연최대치 강우자료를 이용하여 빈도별 혹은 지속기간별 확률강우량을 구한 후 이 값들을 선형 혹은 비선형식의 형태로 회귀분석하여 구하게 된다. 그러나, 이와 같이 회귀분석을 이용하여 추정된 강우강도식은 원래의 강우자료가 가지고 있는 확률적인 특성을 재현한다고 하기는 어렵기 때문에, 본 연구에서는 연최대치 강우자료에 대한 적정 확률분포형으로부터 직접 강우 강도식을 유도하는 방법을 적용하여 대상지역 강우강도식의 매개변수를 산정하였다. 선정된 적정 확률분포형을 이용하여 강우강도식의 매개변수를 추정하는데 있어서, 평균제곱오차의 제곱근을 최소화하는 형태의 목적함수를 구성한 후 유전자알고리즘을 이용하여 적절한 매개변수를 산정하였다. 산정된 매개변수를 사용한 강우강도식으로 구한 결과값과 기존의 강우강도식에 의한 결과값 그리고 지점빈도해석에 의한 결과값을 비교하여 본 연구에서 산정된 강우강도식의 적용성을 평가해 보았다.

• Journal of the Korea Society for Simulation
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• v.27 no.3
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• pp.99-107
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• 2018

In this paper, we study target tracking in two dimensional space using a Extended Kalman filter(EKF), various Extended Kalman Filter and Cost-Reference Particle Filter(CRPF), which can effectively estimate the state values of nonlinear measurement equation. We introduce various Extended Kalman Filter which the Unscented Kalman Filter(UKF), the Central Difference Kalman Filter(CDKF), the Square Root Unscented Kalman Filter(SR-UKF), and the Central Difference Kalman Filter(SR-CDKF). In this study, we calculate Mean Square Error(MSE) of each filters using Monte-Carlo simulation with unknown noise statistics. Simulation results show that among the various of Extended Kalman filter, Square Root Central Difference Kalman Filter has the best results in terms of speed and performance. And, the Cost-Reference Particle Filter has an advantageous feature that it does not need to know the noise distribution differently from Extended Kalman Filter, and the simulation result shows that the excellent in term of processing speed and accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.884-889
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• 2021

The number of people infected with Covid-19 in Korea seemed to be gradually decreasing thanks to various efforts such as social distancing and vaccines. However, just as the number of infected people increased after a particular incident on February 20, 2020, the number of infected people has been increasing rapidly since December 2020 by approximately 500 per day. Therefore, the future Covid-19 is predicted through the Prophet algorithm using Kaggle's dataset, and the explanatory power for this prediction is added through the coefficient of determination, mean absolute error, mean percent error, mean square difference, and mean square deviation through Scikit-learn. Moreover, in the absence of a specific incident rapidly increasing the cases of Covid-19, the proposed method predicts the number of infected people in Korea and emphasizes the importance of implementing epidemic prevention and quarantine rules for future diseases.